Data-Intensive Computing With Hadoop Introduction:
Data-intensive computing is a class of parallel computing applications which use a data parallel approach to processing large volumes of data typically terabytes or petabytes in size and typically referred to as big data.
Overview Of Data-Intensive Computing With Hadoop Job Support:
The Data intensive computing is collecting, managing, analyzing, & understanding data at volumes & rates that push the frontier of the current technologies.The Big Data is the hottest trend in the current business & IT world right now.
We are living in the age of the big data where due to the rapid development in the computational power & the WWW, we are producing an overwhelming amount of data, which has led to the need of an change in the existing architectures & mechanisms of the data processing systems. The Big data- as these large chunks of data is generally called has the redefined the current data processing scenario.
The Data sets of increasing volume & the complexity are often very difficult to process with the standard HPC or the DBMS technology. Large-scale data processing is particular popular in the fields of the linguistics, data mining, machine learning, bioinformatics & the social sciences, but certainly not limited to those disciplines.
The open-source frameworks such as Apache Spark & Hadoop have been developed with this challenge in mind & can be of great benefit for the data-intensive computing